A new distributional approach: estimation, Monte Carlo simulation and applications to the biomedical data sets

Mustafa Kamal; Meshayil M. Alsolmi; Nayabuddin; Aned Al Mutairi; Eslam Hussam; Manahil SidAhmed Mustafa; Said G. Nassr; Mustafa Kamal; Meshayil M. Alsolmi; Nayabuddin; Aned Al Mutairi; Eslam Hussam; Manahil SidAhmed Mustafa; Said G. Nassr

doi:10.3934/nhm.2023069

Networks and Heterogeneous Media

2023, Volume 18, Issue 4: 1575-1599. doi: 10.3934/nhm.2023069

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Research article

A new distributional approach: estimation, Monte Carlo simulation and applications to the biomedical data sets

1.
Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam, 32256, Saudi Arabia
2.
Department of Mathematics, College of Science and Arts at Khulis, University of Jeddah, Jeddah, Saudi Arabia
3.
Department of Epidemiology, College of Public Health and Tropical Medicine, Jazan University, Saudi Arabia
4.
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
5.
Department of Mathematics, Faculty of Science, Helwan University, Egypt
6.
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk Saudi Arabia
7.
Department of Statistics and Insurance, Faculty of Commerce, Arish University, Al-Arish 45511, Egypt

Received: 18 May 2023 Revised: 18 July 2023 Accepted: 21 July 2023 Published: 22 August 2023

This paper introduces the generalized exponential-$ U $ family of distributions as a novel methodological approach to enhance the distributional flexibility of existing classical and modified distributions. The new family is derived by combining the T-$ X $ family method with the exponential model. The paper presents the generalized exponential-Weibull model, an updated version of the Weibull model. Estimators and heavy-tailed characteristics of the proposed method are derived. The new model is applied to three healthcare data sets, including COVID-19 patient survival times and mortality rate data set from Mexico and Holland. The proposed model outperforms other models in terms of analyzing healthcare data sets by evaluating the best model selection measures. The findings suggest that the proposed model holds promise for broader utilization in the area of predicting and modeling healthcare phenomena.
- Weibull distribution,
- heavy-tailed distributions,
- T-X family,
- healthcare sector,
- data modeling,
- China,
- Mexico,
- Holland
Citation: Mustafa Kamal, Meshayil M. Alsolmi, Nayabuddin, Aned Al Mutairi, Eslam Hussam, Manahil SidAhmed Mustafa, Said G. Nassr. A new distributional approach: estimation, Monte Carlo simulation and applications to the biomedical data sets[J]. Networks and Heterogeneous Media, 2023, 18(4): 1575-1599. doi: 10.3934/nhm.2023069

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Abstract

This paper introduces the generalized exponential-$ U $ family of distributions as a novel methodological approach to enhance the distributional flexibility of existing classical and modified distributions. The new family is derived by combining the T-$ X $ family method with the exponential model. The paper presents the generalized exponential-Weibull model, an updated version of the Weibull model. Estimators and heavy-tailed characteristics of the proposed method are derived. The new model is applied to three healthcare data sets, including COVID-19 patient survival times and mortality rate data set from Mexico and Holland. The proposed model outperforms other models in terms of analyzing healthcare data sets by evaluating the best model selection measures. The findings suggest that the proposed model holds promise for broader utilization in the area of predicting and modeling healthcare phenomena.

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